Growing and Maintaining Customer Base While
A Bioinformatics Core Matures: Growing and maintaining customer base while approaching cost-recovery James Cavalcoli, PhD University of Michigan Ann Arbor Outline • Cost recovery • Expanding services • Identifying new customers • Retention of Customers and Staff/Faculty UM Bioinformatics Core Bioinformatics: The science of analyzing and interpreting complex biological data The Bioinformatics Core was created in CCMB (ca. 2009) and established as a Biomedical Research Core Facility by the Office of Research in 2012 Our mission is to meet the growing bioinformatics analysis needs of researchers We help interpret complex, high-throughput biological data (DNA, RNA, Protein) 3 Cost recovery • In general, Cores at UM are supposed to be selfsupporting through recharges back to researchers • Bioinformatics Core’s major costs are salaries – software, cluster usage – Hardware - servers Cost recovery - hurdles • Developing methods and pipelines without a specific paying project. • Accurately tracking time on custom projects • Being able to charge enough to cover costs without breaking investigators banks. Recharge 35% Methods Develop ment 65% Cost recovery – cont’d • Service charges need to include the cost of developing the pipelines / automation for repeatable tasks • Need to recover the time for “learning” a new method, not just the time to run the method • Accurate estimation of time needed is difficult to obtain • Project management effort too ! 6 4/22/2015 Current Trend in Growth Growth of Core Utilization 120 • Revenue was $221,558. Goal was 265K – pretty close !! • # Projects and Investigators are on the right trajectory • Changes in revenue from recharge rate restructuring and from increase in projects too 100 80 60 40 20 0 Schools Departments Investigators Projects 2011 3 12 13 16 2012 5 27 20 25 2013 4 24 33 55 2014 4 26 66 109 Designing Services • Identifying repeatable tasks – A process/method that many will use • Quantitating time / effort into a per sample cost – Resistance and questioning of “hourly” charges – Incorporating effort to develop the repeatable pipeline • Estimating time/effort for novel / custom projects – Custom projects have differential risk, and are assigned to bins of size/risk/effort Expanding Services • New methods come out (Tophat, STAR, etc) • New Technology / platform (PacBio, minIon?) • New utilization / request from researchers (gene panels, Hi-C) ** Need to validate new methods to insure proper results are given and reproducible. When to expand? • When is a new service warranted?? – The genomics core is adding a platform – Multiple requests for the same type of analysis • When do you turn a service into a pipeline? – Need to improve the time to deliver, reduce internal effort – Large increase in requests and samples How to Expand Customer Base? • Roadshows !! – Present at departmental faculty meetings – Describe the core, services – Give examples of projects we’ve worked on • Recruit / Identify External Customers – Other colleges and universities – Biotech / Commercial • Create a regional resource ? We help you throughout your research lifecycle Project Planning Experiment Design Grant Support (Letters) Sequencing Methods Education and Training Data Requirements Execution Data Analysis Options Validation Support Methods/Softwa re Development Post-review Follow-up Analysis Annotation, Integration, Visualization Results Interpretation 12 Publication Data Analysis Sample Size Roadshow example slides Our Standard Services 13 Genome Assembly Identify disease associated variations in DNA Analyze gene expression and alternative splicing Detect regulatory elements and chromatin structure Explore genomes of unsequenced organisms Types DNA Methylation and Protein Binding Whole Genome Seq (WGS) Exome Seq (WES) Amplicon RNA-Seq Microarray ChIP-Seq MeDIP-Seq RRBS Methods RNA Expression Alignment, Variant identification Alignment, Transcript assembly Differential expression Alignment, Peak/signal detection De-novo assembly, Homology mapping, gene prediction Results Genome Re-sequencing Nucleotide polymorphisms, Copy number and structural variations Differential gene expression, Alternative Splicing, Novel transcripts Transcription factor binding, Histone modification, Methylation state Draft genome, Contigs, Functional annotation De-novo sequencing Metagenomics Custom Support for your Research When you need something beyond an established support service … Come talk with us ! Research Support • Develop new methods (amplicon seq; pangenome assembly) • Implement novel analysis workflows (RNA-DNA Editing ) • Evaluate and integrate third-party tools and data sources (Test a new aligners or variant callers) • Specific analysis for publication figures New algorithms and software • Data integration and visualization (e.g. Jacquard, Winnow) • Database design and implementation (DB for RNA-seq metadata and results; Kretzler) 14 Genomic Drivers in Leukemia and Lymphoma (Sami Malek) 2012 - 2014 : 6 projects (4 Exome-Seq, 1 Gene Panel,1 Custom), 152 samples Functional Impact of novel coding variants (SNPS, InDels) Sample Pos ID Somatic Variant Detection Sample 1 chr11 Exome-Seq FL, CLL Ref/Alt A /G Var Effect Type SNP Sample 1 chr11 C/T SNP Sample 2 chr10 -/T IINS Sample 2 chr10 TTT/- DEL Class STOP GAINED AA NONSENSE E104* NON SYNONYMOU MISSENSE S CODING NON SYNONYMOU MISSENSE S CODING NON SYNONYMOU MISSENSE S CODING GENE Gene A N3173D Gene A D361H Gene B E306K Gene B • Novel coding variants identified • Validated with Sanger sequencing Gene-wise listing of variants across 60 samples with functional impact Targeted Gene Panel Prevalence of targeted variants in patient populations 10 genes 60 FL samples Custom Analysis Publication Support 15 Analysis Request • How well do Exome SNP calls match up to SNP6.0 array calls? Method Result • Exome Sensitivity/Specificity analysis against SNP6.0 • Exome SNP calls had 90% sensitivity and 99% specificity Cross-platform Comparison (Exome vs. Microarray) How to maintain growth and quality? • Core service offerings need to managed effectively to insure: – – – – Uniformity of service (cost, time) Quality and reproducibility Resource allocation Customer satisfaction Project & Portfolio Management Customer Retention • • • • • Personal service Informed data handoff; Quality of data Clarity of deliverables and costs Responsiveness to (reasonable) requests Discussing the science before and after the experiment and analysis (Collaboration!) 17 4/22/2015 Staff/faculty Retention • Avoiding boredom and burnout – New Challenges; continuous learning • Building teamwork and cross-training. – Maintain a ‘research’ experience where the core can drive the science • Competing with Industry salaries? – University vs. Commercial – tough to do – Make sure the environment is so good they won’t leave! Bioinformatics Core Team Analysis Team Rich McEachin Ana Grant Manjusha Pande Development Team Weisheng Wu Rajasree Menon Outreach & Training Chris Gates Pete Ulintz Marci Brandenburg Jessica Bene Operations Management Jim Cavalcoli Director Ashwini Bhasi 19 Summary • Cost Recovery improved by good time tracking, appropriate rate structures; increasing throughput • Improving and expanding services is a constant challenge • Outreach is essential for new customer ID • Collaboration and listening to existing customers improves retention • Keeping your staff/faculty engaged is important to longterm consistency Thank you ! The UM Bioinformatics Core is supported by the Office of Research, Medical School Administration and the Biomedical Research Core Facilities at the University of Michigan School of Medicine. 21 4/22/2015
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